The second class is multi-module one-step, which extracts entities and relations in parallel, and then combines them into triples.
For example,
[1]}, [2]}, [3]}, [4]} treat entity recognition and relation classification as a table-filling problem, where each entry represents the interaction between two individual words.
[5]} formulate the joint extraction task as a set prediction problem, avoiding considering the prediction order of multiple triples.
However, due to insufficient mutual constraints between entities and relations in the separate recognition process, such multi-module one-step approaches cannot fully capture the dependencies between predicted entities and relations, resulting in redundant information during triple construction.
